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Record W1908505866 · doi:10.1590/0004-282x20150156

Screening of cognitive impairment in patients with Parkinson's disease: diagnostic validity of the Brazilian versions of the Montreal Cognitive Assessment and the Addenbrooke's Cognitive Examination-Revised

2015· article· en· W1908505866 on OpenAlexaboutno aff

Bibliographic record

VenueArquivos de Neuro-Psiquiatria · 2015
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsCognitive impairmentMontreal Cognitive AssessmentDementiaCognitionCognitive Assessment SystemCognitive disorder

Abstract

fetched live from OpenAlex

OBJECTIVE: The aim of the present study is to examine the accuracy of the Brazilian versions of the Montreal Cognitive Assessment (MoCA) and the Addenbrooke's Cognitive Examination-Revised (ACE-R) to screen for mild cognitive impairment (PDMCI) and dementia (PDD) in patients with Parkinson's disease (PD). METHOD: Both scales were administered to a final convenience sample of 79 patients with PD. Patients were evaluated by a neurologist, a psychiatrist and a neuropsychologist using UPDRS, Hoehn and Yahr and Schwab and England scales, global deterioration scale, a psychiatric structured interview, Mattis Dementia Rating Scale and other cognitive tests. RESULTS: There were 32 patients with PDMCI and 17 patients with PDD. The MoCA and the ACE-R were able to discriminate patients with PDD from the others. CONCLUSION: Both scales showed to be useful to screen for dementia but not for mild cognitive impairment in patients with PD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.271
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations30
Published2015
Admission routes1
Has abstractyes

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